Mapping Trajectories of Alzheimer's Progression via Personalized Brain Anchor-nodes

通过个性化大脑锚节点绘制阿尔茨海默病的进展轨迹

• 批准号: 10571842
• 负责人: Gang Li
• 金额: $ 54.68万
• 依托单位: UNIVERSITY OF TEXAS ARLINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10571842
• 关键词: Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease pathology; Anatomy; Atlases; Biology; Brain; Brain Mapping; Classification; Clinical; Computational Technique; Data; Data Set; Data Sources; Dementia; Development; Disease Progression; Early Diagnosis; Functional disorder; Heterogeneity; Hour; Human; Image; Impairment; Individual; Magnetic Resonance Imaging; Maps; Measures; Methodology; Methods; Modeling; Monitor; Multimodal Imaging; Nature; Neurodegenerative Disorders; Pathway interactions; Patients; Pattern; Phase; Population; Process; Property; Series; Shapes; Structure; Surface; System; Testing; Time; Trees; Work; biobank; brain based; cohort; computerized tools; connectome; cost; deep learning; disease classification; flexibility; image registration; imaging biomarker; imaging study; improved; individual variation; inter-individual variation; large scale data; multimodality; neural network; neuroimaging; normal aging; personalized diagnostics; personalized predictions; pre-clinical; response; tool; trend

Project Summary Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disorder, not only in pathophysiology, but also at different disease progression stages. Despite numerous studies that have investigated the clinical utility of magnetic resonance imaging (MRI) based biomarkers in characterizing AD stages from asymptomatic to mildly symptomatic to dementia, making a personalized precision prediction and early diagnosis of AD is still challenging. Existing imaging biomarkers are limited in representing significant heterogeneity across different individuals and at different clinical stages. This challenge originates from the lack of reliable brain landmarks that can simultaneously characterize and represent robust population correspondences and individual variation during normal aging and AD progression. In response, this project aims to: 1) Identify a set of brain anchor- nodes as population landmarks based on both group-wise consistent patterns and individualized anatomical and connectivity properties during normal aging and AD progression among massive, publicly available neuroimaging data sources; 2) Develop an efficient individualized shape transformation approach based on deep learning to map population anchor-nodes to individual brains by flexibly leveraging multimodal individual features; and 3) Construct a progression tree using anchor-nodes derived brain measures to unveil and represent the wide spectrum of AD development. Individual subjects can thus be projected to the tree structure to effectively and conveniently access their clinical status and predict the trend of AD progression. We will test our new frameworks on four large independent aging/AD cohorts including HCP-Aging, UK Biobank, ADNI and the latest stage of Open Access Series of Imaging Studies (OASIS-3), and freely release our computational tools and processed data to the public.

项目摘要 阿尔茨海默氏病（AD）是一种异质性神经退行性疾病，不仅在病理生理学中，而且在 在不同的疾病进展阶段。尽管进行了许多研究，这些研究调查了 基于磁共振成像（MRI）的生物标志物在表征从无症状到轻度的AD阶段 对痴呆症的症状，做出个性化的精度预测和AD的早期诊断仍在 挑战。现有的成像生物标志物在代表不同的不同异质性方面受到限制 个人和不同的临床阶段。该挑战源于缺乏可靠的脑部标记 可以轻松地表征并表示强大的人口对应关系和个体变异 在正常衰老和AD进展过程中。作为回应，该项目的目的是：1）确定一组大脑锚定 - 节点作为人口地标，基于群体的一致模式和个性化的解剖学和 正常衰老期间的连通性特性和广泛可用的神经影像学的AD进展 数据源； 2）开发一种基于深度学习的有效的个性化形状转化方法 通过灵活利用多模式的个体特征，将种群锚节点映射到个体大脑。 3） 使用锚节点衍生的大脑措施构建进展树，以揭露并表示范围 广告发展的范围。因此，可以将个别受试者投射到树结构上，以有效地 方便地访问其临床状况并预测AD进展的趋势。我们将测试我们的新框架 在四个大型独立老龄化/广告群中 开放访问一系列成像研究（OASIS-3），并自由发布我们的计算工具并处理 向公众数据。